I simply cannot keep up when I write notes by hand -- but I type very quickly. I too have had trouble with equations, and my solution works for me, but isn't the best. Here's my approach, fwiw.

Type notes on my Eee, it has a 7-8 hr (actual) battery, and a relatively quiet keyboard, so as not to be a distraction. I can watch the instructor and the board while typing, unlike handwriting. Using emacs under windows at the moment (better battery life pending some driver updates for linux)

I keep a notepad for any graphs, and I just number any figures I draw. I switch between keyboard and pen as rarely as possible.

Equations are important -- I'm a math major now! For some classes, equations will come as fast as the instructor can scrawl them on the board. I write in an abbreviated shorthand, basically supremely-lazy latex. I neglect anything that could be implicit, and write, for example, omg and Omg instead of \omega and \Omega. I only started a little while back, and I still adjust my abbreviations as I go. That means it's not really parsable yet, but I am doing a good job of figuring out the minimum number of key presses to say what I need to say. After writing it for a while, at least it's easy to read (much like latex, you'll start to see it without needing to render it before long).

I like the mention of cameras. I've started doing that for meetings with my advisor, just using my iPhone to capture the whiteboard after we've gone back and forth on a few ideas.

I've been through N (where N is large) years of school, but only in this last year have I switched to typing. I have a great deal of trouble with handwritten anything (random word and letter transpositions everywhere, HORRIBLE if you're trying to write a mathematical theorem). Switching to typed notes has been one of the best decisions I ever made wrt taking classes.

For different people, different things work. If I just listen, I follow well, but forget quickly. If I take notes, I struggle more to keep up, but I remember things well -- even if I throw my notes away.

Actually, that's really important, but it's talking more about the software than the hardware.

For most problems, it can be very difficult to find a way to write the program so that it efficiently utilizes many processors at once. Typically, a poorly-written MPI (or PVM, or whatever distributed computing model you use) program will leave many CPUs idling much of the time. For some problems the division of labor is fairly straightforward, but for most it's not.

Now, this comment implies that the code is well-written for a cluster. It could also just have CPUs spinning around on redundant calculations. If the size of sending a chunk of data outstrips the cost of computing it (for example), you might recompute it locally, even if that would be redundant work. Note that this isn't necessarily a bad thing, it just means that this is only related to parallel efficiency, not equivalent.

Anyway, I'm babbling, but sorry, I live and breath this stuff these days. In short, a point like "well above 90% of each processor core" is commonly taken to imply that the parallel efficiency is pretty damn good. To summarize even further, sexy!

These are by no means comprehensive. There are *thousands* of states of matter, when one uses precise definitions. Metallic, insulating, superconducting, liquid crystals (of so many types), ferromagnetics, anti-ferromagnets, etc. etc.